Artificial intelligence and machine learning are being deployed in utility cybersecurity to move beyond traditional, rule-based defenses toward adaptive, data-driven detection and countermeasures. Real-time analysis of network and operational data helps identify subtle anomalies and emerging threats across IT and OT environments, while automation reduces manual workload and accelerates remediation. The increasing digital integration of legacy power infrastructure with smart grids, IoT devices, and third-party systems has significantly expanded the attack surface, making static defenses insufficient against advanced adversaries. AI-powered systems provide continuous monitoring and unified visibility across the entire utility infrastructure, helping bridge security gaps that arise from resource constraints and understaffed teams in many utilities.
A layered architecture of AI technologies strengthens smart grid cybersecurity by combining multiple capabilities. Predictive analytics from conventional AI and deep learning models anticipate threat patterns, while generative AI assists human defenders with contextual insights. Graph-based machine learning correlates disparate security events to detect complex attacks, and hyperautomation extends SOAR capabilities to execute operational tasks with minimal human input. More advanced agentic AI introduces autonomous prioritization and proactive defense functions, enhancing threat triage and response efficiency. This multi-layered approach enables security operations teams to shift from reactive incident handling to proactive resilience, improving the speed, scalability, and effectiveness of defenses across critical power infrastructure.

Source: https://www.powermag.com/the-five-layers-of-ai-safeguarding-the-utility-industry/